Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
If you've been building with Supabase, you know their Storage API is fantastic for web apps. But sometimes, you just need your files on your local machine—whether for a manual backup, bulk editing, or migrating data. While you could write a script using the Supabase SDK, there is a much faster, "no-code" way to manage your files like a Pro: Cyberduck. Note: Cyberduck is an official Supabase partne
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E